###仅为自己练习，没有其他用途

  1 import torch

 import torch.nn as nn

 import torch.utils.data as Data

 import torchvision

 import matplotlib.pyplot as plt

 from mpl_toolkits.mplot3d import Axes3D

 from matplotlib import cm

 import numpy as np

 # torch.manual_seed(1)    # reproducible

 # Hyper Parameters

 EPOCH = 10

 BATCH_SIZE = 64

 LR = 0.005         # learning rate

 DOWNLOAD_MNIST = False

 N_TEST_IMG = 5

 # Mnist digits dataset

 train_data = torchvision.datasets.MNIST(

     root='./mnist/',

     train=True,                                     # this is training data

     transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to

                                                     # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]

     download=DOWNLOAD_MNIST,                        # download it if you don't have it

 )

 # plot one example

 print(train_data.train_data.size())     # (60000, 28, 28)

 print(train_data.train_labels.size())   # (60000)

 plt.imshow(train_data.train_data[2].numpy(), cmap='gray')

 plt.title('%i' % train_data.train_labels[2])

 plt.show()

 # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)

 train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

 class AutoEncoder(nn.Module):

     def __init__(self):

         super(AutoEncoder, self).__init__()

         self.encoder = nn.Sequential(

             nn.Linear(28*28, 128),

             nn.Tanh(),

             nn.Linear(128, 64),

             nn.Tanh(),

             nn.Linear(64, 12),

             nn.Tanh(),

             nn.Linear(12, 3),   # compress to 3 features which can be visualized in plt

         )

         self.decoder = nn.Sequential(

             nn.Linear(3, 12),

             nn.Tanh(),

             nn.Linear(12, 64),

             nn.Tanh(),

             nn.Linear(64, 128),

             nn.Tanh(),

             nn.Linear(128, 28*28),

             nn.Sigmoid(),       # compress to a range (0, 1)

         )

     def forward(self, x):

         encoded = self.encoder(x)

         decoded = self.decoder(encoded)

         return encoded, decoded

 autoencoder = AutoEncoder()

 optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)

 loss_func = nn.MSELoss()

 # initialize figure

 f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))

 plt.ion()   # continuously plot

 # original data (first row) for viewing

 view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.

 for i in range(N_TEST_IMG):

     a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())

 for epoch in range(EPOCH):

     for step, (x, b_label) in enumerate(train_loader):

         b_x = x.view(-1, 28*28)   # batch x, shape (batch, 28*28)

         b_y = x.view(-1, 28*28)   # batch y, shape (batch, 28*28)

         encoded, decoded = autoencoder(b_x)

         loss = loss_func(decoded, b_y)      # mean square error

         optimizer.zero_grad()               # clear gradients for this training step

         loss.backward()                     # backpropagation, compute gradients

         optimizer.step()                    # apply gradients

         if step % 100 == 0:

             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())

             # plotting decoded image (second row)

             _, decoded_data = autoencoder(view_data)

             for i in range(N_TEST_IMG):

                 a[1][i].clear()

                 a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')

                 a[1][i].set_xticks(()); a[1][i].set_yticks(())

             plt.draw(); plt.pause(0.05)

 plt.ioff()

 plt.show()

 # visualize in 3D plot

 view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.

 encoded_data, _ = autoencoder(view_data)

 fig = plt.figure(2); ax = Axes3D(fig)

 X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()

 values = train_data.train_labels[:200].numpy()

 for x, y, z, s in zip(X, Y, Z, values):

     c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)

 ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())

 plt.show()